In recent years, empirical Bayesian (EB) inference has become an attract...
In this paper, an ontology-based approach is used to organize the knowle...
The determination of the number of mixture components (the order) of a f...
Mixture-of-experts (MoE) models are a popular framework for modeling
het...
The class of location-scale finite mixtures is of enduring interest both...
The soft-margin support vector machine (SVM) is a ubiquitous tool for
pr...
Mixtures-of-Experts models and their maximum likelihood estimation (MLE)...
Approximate Bayesian computation (ABC) has become an essential part of t...
Concentration inequalities have become increasingly popular in machine
l...
Given sufficiently many components, it is often cited that finite mixtur...
In linear models, the generalized least squares (GLS) estimator is appli...
We investigate the sub-Gaussian property for almost surely bounded rando...
In this paper, we introduce methods of encoding propositional logic prog...
After the 2016 double dissolution election, the 45th Australian Parliame...
The mitigation of false positives is an important issue when conducting
...
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric
...
Kernel density estimators (KDEs) are ubiquitous tools for nonpara- metri...
Randomized neural networks (NNs) are an interesting alternative to
conve...
The problem of complex data analysis is a central topic of modern statis...
A new maximum approximate likelihood (ML) estimation algorithm for the
m...
Mixture-of-experts (MoE) models are a powerful paradigm for modeling of ...
Graph algorithms and techniques are increasingly being used in scientifi...
Support vector machines (SVMs) are an important tool in modern data anal...
MM (majorization--minimization) algorithms are an increasingly popular t...
The mixture of experts (MoE) model is a popular neural network architect...